Approaches on crowd counting and density estimation: a review

B Li, H Huang, A Zhang, P Liu, C Liu - Pattern Analysis and Applications, 2021 - Springer
In recent years, urgent needs for counting crowds and vehicles have greatly promoted
research of crowd counting and density estimation. Benefiting from the rapid development of …

Distribution matching for crowd counting

B Wang, H Liu, D Samaras… - Advances in neural …, 2020 - proceedings.neurips.cc
In crowd counting, each training image contains multiple people, where each person is
annotated by a dot. Existing crowd counting methods need to use a Gaussian to smooth …

A generalized loss function for crowd counting and localization

J Wan, Z Liu, AB Chan - … of the IEEE/CVF conference on …, 2021 - openaccess.thecvf.com
Previous work shows that a better density map representation can improve the performance
of crowd counting. In this paper, we investigate learning the density map representation …

NWPU-crowd: A large-scale benchmark for crowd counting and localization

Q Wang, J Gao, W Lin, X Li - IEEE transactions on pattern …, 2020 - ieeexplore.ieee.org
In the last decade, crowd counting and localization attract much attention of researchers due
to its wide-spread applications, including crowd monitoring, public safety, space design, etc …

Transcrowd: weakly-supervised crowd counting with transformers

D Liang, X Chen, W Xu, Y Zhou, X Bai - Science China Information …, 2022 - Springer
The mainstream crowd counting methods usually utilize the convolution neural network
(CNN) to regress a density map, requiring point-level annotations. However, annotating …

Jhu-crowd++: Large-scale crowd counting dataset and a benchmark method

VA Sindagi, R Yasarla, VM Patel - IEEE transactions on pattern …, 2020 - ieeexplore.ieee.org
We introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++)
that contains “4,372” images with “1.51 million” annotations. In comparison to existing …

Spatial uncertainty-aware semi-supervised crowd counting

Y Meng, H Zhang, Y Zhao, X Yang… - Proceedings of the …, 2021 - openaccess.thecvf.com
Semi-supervised approaches for crowd counting attract attention, as the fully supervised
paradigm is expensive and laborious due to its request for a large number of images of …

Cnn-based density estimation and crowd counting: A survey

G Gao, J Gao, Q Liu, Q Wang, Y Wang - arXiv preprint arXiv:2003.12783, 2020 - arxiv.org
Accurately estimating the number of objects in a single image is a challenging yet
meaningful task and has been applied in many applications such as urban planning and …

Ha-ccn: Hierarchical attention-based crowd counting network

VA Sindagi, VM Patel - IEEE Transactions on Image Processing, 2019 - ieeexplore.ieee.org
Single image-based crowd counting has recently witnessed increased focus, but many
leading methods are far from optimal, especially in highly congested scenes. In this paper …

Multi-level bottom-top and top-bottom feature fusion for crowd counting

VA Sindagi, VM Patel - Proceedings of the IEEE/CVF …, 2019 - openaccess.thecvf.com
Crowd counting presents enormous challenges in the form of large variation in scales within
images and across the dataset. These issues are further exacerbated in highly congested …